ENGLISH

12卷/3期

12卷/3期

華藝線上圖書館

Pages:

197-216

論文名稱

小波演算法重建點雲覆蓋面

Title

A Wavelet Algorithm for DSM Reconstruction by Using 3D Point Cloud

作者

蔡展榮,嚴晟瑋

Author

Jaan-Rong Tsay,Sheng-Wei Yen

中文摘要

本文提出一個重建點雲覆蓋面的演算法,它應用具備碎形表達能力的二維三階Daubechies 小波之尺 度函數來組成每一個點的觀測方程式,運用由粗而細的求解策略,並使用二元格點位置上的虛擬觀測量 PHO 及POI 來解決最小二乘平差求解過程常出現的劣態問題。同時,本文也提出了一個全自動重新給權 的模式,來降低改正數絕對值大於兩倍先驗高程精度的光達點高程觀測值之權值;空載光達點雲實驗結 果驗證了此法已有效降低「吉布斯效應」之影響,而且當二元格點的點間隔(1.25m)與點雲之平均點間距(約 1m)相當時,後驗單位權中誤差為±20~23cm,已相當於點雲的先驗高程精度±25cm。

Abstract

This paper proposes an algorithm for reconstruction of an object surface covered with 3D points. It utilizes the 2D Daubechies scaling functions of 3rd order, which can describe fractal geometry, to derive the observation equation for each point. The linear system is then solved by the least-squares adjustment (LSA) and the reconstructed surface can then be generated. To overcome the ill-posed problem which often emerges in LSA, we employ a from-coarse-to-fine strategy and use the pseudo observations on dyadic points, called POI (Pseudo Observations by Interpolation) and PHO (Pseudo Height Observations). Moreover, a full-automated weighting model is proposed in order to eliminate the so-called Gibbs effect. It reduces the weights of the points whose absolute residuals are larger than twice the a priori height accuracy of the LIDAR point. Some tests are done by using airborne LIDAR points. They verify that the artifacts caused by the Gibbs phenomenon can be eliminated to the large extent by combining the pseudo observations and the weighting model. While the dyadic points have approximately the point interval of LIDAR points, the a posteriori standard deviations of unit weight in our tests are about ±20~23cm which are all to the extents of the a priori height accuracy, ±25cm.

關鍵字

物面重建、空載光達點雲,小波,劣態條件,吉布斯效應

Keywords

object surface reconstruction, airborne LIDAR point cloud, wavelets, ill-posed condition, Gibbs effect

附件檔名

華芸線上圖書館

N / A

備註說明

200709-12-3-197-216

Pages:

217-224

論文名稱

最小二乘影像匹配與其精度改進

Title

Least Squares Image Matching and Its Accuracy Improvements

作者

吳究,張奇,王佳珮

Author

Joz Wu,Chi Chang,Chia-Pei Wang

中文摘要

標準最小二乘影像匹配法之函數模式通常擁有輻射平移和尺度參數、及幾何仿射參數。本文旨於改 進傳統的隨機模式,不再視差分的灰階為獨立且分布相同的變數。尺度性方差與協方差分量各派定給處 理後的影像區塊。經估計所得之(協)方差分量續用以重新定義觀測量的協方差矩陣,並迭代平差相對的權 值,直至獲得穩定的參數值為止。理論上,本文所介紹的估計式(Blue-estimator)雷同於最佳不變二次無偏 估計式。實務上藉兩幅Radarsat-1 合成口徑雷達影像,以探討所提影像匹配法之應用性;特徵對象如池塘 的轉折角、和道路交义口。結果顯示,線列與取樣像坐標之匹配精度,得以提昇0.2~0.4 個像元。

Abstract

Usually, a standard least-squares image-matching functional model has radiometric shift and drift parameters, and geometric affinity parameters. This paper is focused to improve on a conventional stochastic modeling. Single-difference gray-levels are no longer dealt with to be independent and identically distributed. Scaling variance and covariance components are associated with some processed image segments. The estimated variance and covariance components are then used to form a new measurement covariance matrix, leading to iteratively adjusted weights until a steady parameter state is achieved. In theory, the proposed Blue-estimator is akin to the best invariant quadratic unbiased estimator. In practice, two Radarsat-1 synthetic aperture radar image scenes were made available to study an image-matching applicability to features such as an angular section of a pond and an intersection of roads. As a result, both the line and sample coordinates can be determined more accurately.

關鍵字

雷達影像匹配,方差分量估計

Keywords

Matching between Radar Images, Estimation of Variance Components

附件檔名

華芸線上圖書館

N / A

備註說明

200709-12-3-217-224

Pages:

225-240

論文名稱

支持向量機應用於水稻田辨識之研究

Title

Rice Paddy Identification Using the Support Vector Machine

作者

陳承昌,史天元

Author

C. C. Chen,T. Y. Shih

中文摘要

「支持向量機」是以統計學習理論為基礎,所建構出的機器學習系統。其基本原理是在特徵空間中尋求具有最大區分度邊界的超平面,以區分不同的二元類別。本研究以「支持向量機」為分類器,進行水稻田的辨識作業,並採用嘉義地區多時段福衞二號(Formosat-2)影像及新竹地區多時段SPOT 影像為資料 來源。「支持向量機」可選用不同核函數,而且會因核函數選用的不同,而對分類成果造成差異。因此, 本研究於嘉義及新竹實驗區分別採用線性、多項式、輻狀基底函數及兩層式類神經網路為核函數進行辨 識作業,以分析其影響。 分類實驗成果,將「支持向量機」與高斯最大似然分類法及輻狀基底函數類神經網路,進行分類成 果比較。由實驗成果顯示,「支持向量機」於嘉義實驗區以2 階多項式所得的分類精度為最佳,其整體精 度為89.830%、Kappa 值為0.79303;於新竹實驗區以輻狀基底函數所得的分類精度為最佳,其整體精度為 84.989%、Kappa 值為0.68269。於兩實驗區中,「支持向量機」的分類精度皆優於高斯最大似然分類法及 輻狀基底函數類神經網路。

Abstract

This study investigates the application of the Support Vector Machine (SVM) for image classification. The images used for the experiment include multi-temporal Formosat-2 images of the Chiayi area and multi-temporal SPOT images of the Hsinchu area. There are a number of kernel functions to be selected with SVM. In experiment, Gaussian Maximum Likelihood Classification and Radial Basis Function (RBF) neural network are used for comparison. The Polynomial Kernel Function is the best for Chiayi and RBF is the best for Hsinchu. The overall accuracy is 89.830% for Chiayi and 84.989% for Hsinchu. The kappa index is 0.79303 for Chiayi and 0.68269 for Hsinchu. In terms of the classification accuracy, Support Vector Machine is shown to be better than Gaussian Maximum Likelihood Classification and Radial Basis Function (RBF) neural network.

關鍵字

多光譜影像、人工智慧

Keywords

Multispectral images, Artificial intelligence

附件檔名

華芸線上圖書館

N / A

備註說明

200709-12-3-225-240

Pages:

241-256

論文名稱

多分類器系統中Bagging and Boosting 法則的改進

Title

Using Modified Bagging and Boosting Algorithms in Multiple Classifiers System for Remote Sensing Image Classification

作者

曾裕強,陳錕山,周念湘

Author

Y. C. Tzeng,Kun-Shan Chen,N. S. Chou

中文摘要

本文針對多分類器系統中提出一修正後的Bagging and Boosting 票決方式來改善遙測影像中地物分類 的精度,並藉由引進一信心指標, 多分類器系統可以增加各分類器成間的差異度或降低模糊度。我們利 用雷達影像與光學影像的融合來測試多分類器系統的分類性能。實驗結果顯示新的多分類器系統可大幅 提升整體的分類精度。

Abstract

In this paper, modified Bagging and Boosting voting methods were proposed in the multiple classifiers system for terrain classification of remote sensing images. The improvement is achieved by introducing a confidence index to reduce the ambiguities among the targets being classified. Performance of the proposed multiple classifiers system was tested using fused radar and optical images. Experimental results show that the classifier is able to substantially improve the classification accuracy.

關鍵字

多分類器系統,遙測影像

Keywords

multiple classifiers system, Bagging and Boosting, remote sensing image

附件檔名

華芸線上圖書館

N / A

備註說明

200709-12-3-241-256

Pages:

257-272

論文名稱

有理函數模式於高解析衛星影像幾何改正之應用

Title

Geometric Correction of High Resolution Satellite Image via Rational Function Model

作者

張智安,陳良健

Author

Tee-Ann Teo,Liang-Chien Chen

中文摘要

由於具標準化及簡潔之特性,有理函數模式廣泛應用於高解析衛星影像之幾何處理。針對高解析衛 星影像幾何改正之有理函數模式可分為兩種形式,第一種是直接法有理函數模式,使用地面控制點直接 計算有理函數係數,建立物像空間轉換關係;第二種是間接法有理函數模式,使用衛星載體資料間接計 算有理函數係數,再以地面控制點精化有理函數模式,完成物像空間轉換關係之建立。本研究之目的為 比較這兩種有理函數模式形式於高解析衛星影像之幾何改正。實驗中使用IKONOS 衛星影像進行分析, 實驗結果顯示,直接法較間接法有理函數模式需較多控制點,直接法與間接法分別需要21 及6 個控制點 可達次像元精度。

Abstract

Due to its standardization and simplicity, rational function model has been widely used in the geometric correction for high resolution satellite images. The rational function model includes direct and indirect methods. The direct method uses the ground control points to determine the rational polynomial coefficients directly. The indirect one uses on-board data to establish the rational polynomial coefficients. Then, a small number of ground control points are applied to refine the model. This investigation compares the geometric precision of direct and indirect rational function model for high resolution satellite images. Test data includes an IKONOS satellite image with 1 meter resolution. Experimental results indicate that, direct method needs more control point when compared to the indirect method. Both of the direct and indirect methods may reach sub-pixel accuracy when 21 and 6 control points are applied, respectively.

關鍵字

幾何校正,有理函數模式,高解析衛星影像

Keywords

Geometric correction; Rational function model; High resolution Satellite

附件檔名

華芸線上圖書館

N / A

備註說明

200709-12-3-257-272

Pages:

275-281

論文名稱

應用影像分割技術與碎形理論於福衛二號 Quick-Look 影像之雲覆蓋萃取

Title

Using Segmentation Techniques and Fractal Theory to Extract Cloud Coverage for Formosat-2 QUICK-LOOK Images

作者

張立雨,陳繼藩,陳哲俊,林欣穎

Author

L. Y. Chang, C. F. Chen, A. J. Chen, H. Y. Lin

中文摘要

對於應用光學衛星影像進行地面各項土地資源調查而言,影響影像可用度之最大因素即為影像之含 雲量。因此對於所接收到之影像若能快速的提供相關雲覆蓋量之評估,不但對於影像使用者在選擇影像 上有更良好之參考,也能在影像接收上提供適當資訊以作為未來接收規劃之依據。目前,福衛二號衛星 影像在雲量評估上主要是利用系統所產生之QUICK-LOOK 影像進行人工判釋,而QUICK-LOOK 影像為衛 星影像接收系統產生作為影像檢索用途之影像。而應用QUICK-LOOK 影像進行雲覆蓋判釋時,主要是先 將原始QUICK-LOOK 影像分割為八等分,並且在每等分中以人工方式給定五種等級之雲覆蓋指標來標示 雲含量之多寡。此項指標除必須花費較多時間進行人工判釋外,並且其結果常會受到操作者之主觀認定 而受影響。同時,僅使用五種等級所產生之雲覆蓋資訊對於實際應用上亦略顯不足。因此本研究提出利 用影像分割並配合碎形理論之分析方法對QUICK-LOOK 影像進行定量且自動化之雲覆蓋評估。方法上主 要先利用區塊成長進行影像分割後,再利用非監督性分類法K-MEAN 將分割後之區塊進行非監督性分類 來產生訓練雲區區塊。最後利用所產生之訓練雲區區塊計算雲區之碎形特性,並以此碎形特性進行所有 雲區區塊之萃取。研究成果顯示,將所產生之結果轉換為相當人工判釋所得之雲量指標後再與人工判釋 之結果比較,其結果顯示兩者具有一致性。

Abstract

The percentage of cloud coverage is generally considered the most important factor to affect the availability of optical satellite images for land resource applications. Consequently, an effective cloud assessment process can not only offer general users a guideline for image browsing, but also provide valuable information for orbit planning of ground receiving station. At present, the cloud coverage of Formosat-2 data is visually examined and manually estimated by the operators on the QUICK-LOOK images. The QUICK-LOOK images basically are the reduced images created by satellite data receiving system for browsing purpose. As to the current estimation of cloud coverage, every QUICK-LOOK image is divided into 8 portions and the cloud coverage for each portion is indicated with a cloud index with 5 grades. Apparently, the visual and manual cloud assessment method is labor-intensive, time-consuming, and dependent on the operator’s experience. Furthermore, the existing 5-grade cloud index is inadequate to describe the accurate percentage of cloud coverage. Thus, there is an urgent need for developing an automatic approach for cloud assessment. In this study, a two-stage scheme based on image segmentation and fractal theory is used to automatically extract cloud pixels from QUICK-LOOK images. The experimental results indicate that the cloud areas extracted by proposed scheme are very similar to the manual interpretation. In addition, when the automatically extracted cloud coverage is transferred to the existing cloud index, a good agreement between our automatic approach and current manual estimation is achieved.

關鍵字

QUICK-LOOK 影像、雲區覆蓋量評估、碎形理論

Keywords

QUICK-LOOK image, Cloud assessment, Fractal theory

附件檔名

華芸線上圖書館

N / A

備註說明

200709-12-3-273-281

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